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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Turbulence-parameter estimation for current-energy converters using surrogate model optimization

Surrogate models maximize information utility by building predictive models in place of computational or experimentally expensive model runs. Marine hydrokinetic current energy converters require large-domain simulations to estimate array efficiencies and environmental impacts. Meso-scale models typically represent turbines as actuator discs that act as momentum sinks and sources of turbulence and its dissipation. An OpenFOAM model was developed where actuator disc k-ε turbulence was characterized using an approach developed for flows through vegetative canopies. Turbine-wake data from laboratory flume experiments collected at two influent turbulence intensities were used to calibrate parameters in the turbulence-source terms in the k-ε equations. Additionally, parameter influences on longitudinal wake profiles were estimated using Gaussian process regression with subsequent optimization minimizing the objective function within 3.1% of those obtained using the full model representation, but for 74% of the computational cost (far fewer model runs). This framework facilitates more efficient parameterization of the turbulence-source equations using turbine-wake data.

16 TIDAL AND WAVE POWER↗
DOE OSTI2013

Uncertainty Quantification in CO 2 Sequestration Using Surrogate Models from Polynomial Chaos Expansion

In this paper, surrogate models are iteratively built using polynomial chaos expansion (PCE) and detailed numerical simulations of a carbon sequestration system. Output variables from a numerical simulator are approximated as polynomial functions of uncertain parameters. Once generated, PCE representations can be used in place of the numerical simulator and often decrease simulation times by several orders of magnitude. However, PCE models are expensive to derive unless the number of terms in the expansion is moderate, which requires a relatively small number of uncertain variables and a low degree of expansion. To cope with this limitation, instead of using a classical full expansion at each step of an iterative PCE construction method, we introduce a mixed-integer programming (MIP) formulation to identify the best subset of basis terms in the expansion. This approach makes it possible to keep the number of terms small in the expansion. Monte Carlo (MC) simulation is then performed by substituting the values of the uncertain parameters into the closed-form polynomial functions. Based on the results of MC simulation, the uncertainties of injecting CO{sub 2} underground are quantified for a saline aquifer. Moreover, based on the PCE model, we formulate an optimization problem to determine the optimal CO{sub 2} injection rate so as to maximize the gas saturation (residual trapping) during injection, and thereby minimize the chance of leakage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Density functional theory-based surrogate kinetic models for heterogeneous reactions of hydrocarbon intermediates on silicon carbide

The increasing demand for high-performance materials in advanced technologies highlights the importance of achieving a fundamental understanding and potential control of silicon carbide (SiC) deposition processes. However, existing models often lack sufficient theoretical detail, relying heavily on empirical data and offering limited predictive capability. In particular, the complex surface chemistry governing SiC growth remains poorly understood. This study addresses these challenges by employing density functional theory (DFT) to investigate key heterogeneous reactions involving hydrocarbon intermediates on SiC surfaces, including dehydrogenation, hydrogenation, and carbon deposition. Transition state searches were conducted to identify reaction pathways and energy barriers. While first-principles calculations offer high accuracy, they are computationally intensive. To extend the utility of these first-principles results, vibrational analyses were performed using phonon-based statistical thermochemistry to compute temperature-dependent reaction rates which were used to develop Arrhenius-type surrogate kinetic models. Furthermore, the resulting framework provides a more rigorous, physically grounded basis for integrating atomistic insights into continuum-scale modeling, ultimately enabling improved prediction and optimization of SiC film growth in high-performance material systems.

Density Functional Theory↗
DOE OSTI2020

ROM-Based Surrogate Systems Modeling of EBR-II

We report the System Analysis Module (SAM), developed and maintained by Argonne National Laboratory, is designed to provide whole-plant transient safety analysis capabilities for a number of advanced non-light water reactors, including sodium-cooled fast reactor (SFR), lead-cooled fast reactor (LFR), and molten salt reactor (MSR)/fluoride-salt-cooled high-temperature reactor (FHR) designs. SAM is primarily constructed as a systems-level analysis tool, with the potential to incorporate reduced order models from three-dimensional computational fluid dynamics (CFD) simulations to improve characterization of complex, multidimensional physics. It is recognized that the computational expense associated with CFD can be intractable for various engineering analyses, such as uncertainty quantification, inference, and design optimization. This paper explores the reducibility of a SAM model using recent advances in randomized linear algebra techniques, which attempt to find recurring patterns in the various realizations generated by a model after randomly perturbing all its input parameters. The reduction is described in terms of fewer degrees of freedom (DOFs), referred to as the active DOFs, for the model variables such as input model parameters and model responses. The results indicate that there is significant room for additional reduction that may be leveraged for additional computational gains when employing SAM for engineering-intensive analyses that require repeated model executions. Different from physics-based reduction approaches, the proposed approach allows one to estimate upper bounds on the reduction errors, which are rigorously developed in this work. Finally, different methods for surrogate model construction, such as regression and neural network-based training, are employed to correlate the input and output active DOFs, which are related back to the original variables using matrix-based linear transformations.

42 ENGINEERING↗
DOE OSTI2017

Sequential Designs Based on Bayesian Uncertainty Quantification in Sparse Representation Surrogate Modeling

A numerical method, called OBSM, was recently proposed which employs overcomplete basis functions to achieve sparse representations. While the method can handle non-stationary response without the need of inverting large covariance matrices, it lacks the capability to quantify uncertainty in predictions. We address this issue by proposing a Bayesian approach which first imposes a normal prior on the large space of linear coefficients, then applies the MCMC algorithm to generate posterior samples for predictions. From these samples, Bayesian credible intervals can then be obtained to assess prediction uncertainty. A key application for the proposed method is the efficient construction of sequential designs. Several sequential design procedures with different infill criteria are proposed based on the generated posterior samples. As a result, numerical studies show that the proposed schemes are capable of solving problems of positive point identification, optimization, and surrogate fitting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Establishing a theory for deuteron induced surrogate reactions

Background: Deuteron-induced reactions serve as surrogates for neutron capture into compound states. Although these reactions are of great applicability, no theoretical efforts have been invested in this direction over the last decade. Purpose: The goal of this work is to establish on firm grounds a theory for deuteron-induced neutron-capture reactions. This includes formulating elastic and inelastic breakup in a consistent manner. Method: We describe this process both in post- and prior-form distorted wave Born approximation following previous works and discuss the differences in the formulation. While the convergence issues arising in the post formulation can be overcome in the prior formulation, in this case one still needs to take into account additional terms due to nonorthogonality. Results: We apply our method to the Nb93(d,p)X at Ed=15 and 25 MeV and are able to obtain a good description of the data. We then look at the various partial wave contributions, as well as elastic versus inelastic contributions. We also connect our formulation with transfer to neutron bound states.Conclusions: Our calculations demonstrate that the nonorthogonality term arising in the prior formulation is significant and is at the heart of the long-standing controversy between the post and the prior formulations of the theory. We also show that the cross sections for these reactions are angular-momentum dependent and therefore the commonly used Weisskopf limit is inadequate. We finally make important predictions for the relative contributions of elastic breakup and nonelastic breakup and call for elastic-breakup measurements to further constrain our model.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Uncertainty-Proof Hosting Capacity with Surrogate Affine Policy

Physical constraints must be enforced when dis-tributed energy resources, such as PV, are integrated into distribution network. Hosting capacity (HC) is thus introduced to define the maximum renewable that distribution system can accommodate. When the grid is further pushed towards low-carbon, many research efforts are devoted to increasing HC. Security and cost-efficiency become even more important in determining HC. This work proposes an uncertainty-proof HC with surrogate affine policy. Flexible resources are leveraged to increase HC. We propose a novel hybrid two-stage AC model with variable uncertainty set. An iterative algorithm is designed to solve the problem. The proposed model and solution approach are validated in modified 141-node feeders, and HC performance is also analyzed.

hosting capacity↗
DOE OSTI2026

Forensic characterization of surrogate nuclear explosion debris: radiochemical and spectroscopic strategies for method validation

Surrogate nuclear explosion debris (SNED) has emerged as a critical platform for advancing post-detonation nuclear forensic analysis in the absence of readily accessible historic materials. SNED enables controlled investigation and validation of analytical methodologies used to interrogate the chemical, isotopic, radiological, and microstructural signatures preserved in nuclear explosion debris. This review presents an integrated assessment of destructive and non-destructive analytical techniques commonly employed within decision-driven nuclear forensic workflows. Each technique is discussed individually while highlighting how it contributes to different stages of post-detonation analysis. Core methods – including gamma and alpha spectrometry, ICP-MS, TIMS, SIMS, SEM-EDS, XRF, LIBS, vibrational spectroscopy, and X-ray absorption spectroscopy – are critically evaluated with respect to forensic maturity, information content, and matrix limitations. Emphasis is placed on the role of SNED in benchmarking multi-modal workflows and identifying gaps in reproducing heterogeneity, fractionation, and radiation-driven evolution relevant to forensic attribution.

X-ray spectroscopic methods↗
DOE OSTI2025

Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation

We introduce a conditional pseudo-reversible normalizing flow (PR-NF) that directly learns conditional probability distributions from noisy physical models to efficiently quantify both forward and inverse uncertainty propagation. Traditional surrogate modeling approaches approximate only the deterministic component of physical models, requiring separate noise characterization and computationally expensive sampling methods for inverse problems. Here, in this work, we develop the conditional PR-NF model to directly learn and efficiently generate samples from the conditional probability density functions (PDFs). The training process utilizes dataset consisting of input-output pairs without requiring prior knowledge about the noise and the function. Once trained, our model efficiently generates samples from conditional PDFs for any input within the training domain. Moreover, the pseudo-reversibility feature allows for the use of fully connected neural network architectures, which simplifies the implementation and enables theoretical analysis. We provide a rigorous convergence analysis of the conditional PR-NF model, showing its ability to converge to the target conditional PDF using the Kullback−Leibler divergence. To demonstrate the effectiveness of our method, we apply it to several benchmark tests and a real-world geologic carbon storage problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio Frequency Heating in Fusion Energy Science

This work presents a detailed case study on using Generative AI (GenAI) to develop AI surrogates for simulation models in fusion energy research. The scope includes the methodology, implementation, and results of using GenAI to assist in model development and optimization, comparing these results with previous manually developed models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Creep and Creep Fracture Modeling with Surrogate Creep Models and the Extended Finite Element Method

Alloy components in advanced nuclear reactors will be subjected to environmental conditions that could include high temperatures, irradiation, and exposure to corrosive salts. These conditions could lead to the formation of crack-like defects, which could grow over time in a mechanism known as creep crack growth (CCG). Predicting growth rates of these defects is important for assessing the safe operating life of advanced reactors. This project documents progress toward developing and testing next-generation data-driven constitutive models for deformation creep. It also documents the application of the extended finite element method in conjunction with surrogate creep models to predict CCG parameters under a variety of conditions. These important incremental developments contribute to the longer-term objective of developing microstructure-aware constitutive models that can be used for predicting creep deformation and CCG at the component scale with improved accuracy.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Surrogate Fuel Chemistry from ReaxFF Molecular Dynamics

Reactive molecular dynamics with ReaxFF simulations are used to study the chemistry of four component jet fuel mixtures of n-dodecane, iso-octane, n-propylbenzene, and mesitylene. ReaxFF does not require knowledge of the chemistry a priori providing insight into reactions otherwise not seen in established chemical kinetic mechanisms. The results show that a difference in the local chemical environment can change the fuel decomposition pathways across the surrogate mixtures where the same fuel species are used. These simulations highlight the need to consider cross-chemical interactions in chemical kinetic mechanism development and the importance of resonance stabilized polyaromatic hydrocarbon reactions for soot growth and formation. A method for predicting the threshold sooting index from the carbon species formed at the end of the simulations is established. The results of the simulations are consistent with measurements of the threshold sooting index with the predicted values within 20% of the measured data

Surrogate Fuels↗
NASA NTRSDate not supplied

Coaxial Rotor CFD Validation and ML Surrogate Model Generation

A fixed-pitch speed-controlled coaxial rotor system was tested in the NASA Langley Transonic Dynamics Tunnel in September 2022. The rotors have a diameter of 1.35 meters and an inter-rotor spacing of 25% of the diameter. Though this test was focused on the NASA New Frontiers Dragonfly mission, the resulting dataset is relevant to a wide array of applications of multirotor vehicles, especially those using fixed-pitch variable-speed rotors. Most notably beyond Dragonfly, perhaps, is the application of coaxial rotor pair configurations for eVTOL and Urban Air Mobility (UAM) aircraft. This work provides a thorough CFD validation study quantifying coaxial rotor performance estimation with accuracy on order 5-10% using an efficient hybrid BEMT-URANS flow solver over a wide range of operating conditions. This accuracy was achieved using novel approaches for the construction of both the C81 airfoil performance lookup tables and the BEMT rotor model. These novel approaches are combined with advanced scripting to further accelerate the commercial off-the-shelf CFD solver on GPU accelerated machines. Finally, the development of machine learning surrogate models is presented to produce highly efficient and accurate rotor performance predictions for fixed-pitch variable-speed multirotor aircraft over the complete flight regime including scout, cruise, climb, descent, as well as limiting cases of vortex-ring and windmill-brake states.

Coaxial↗
DOE OSTI2015

Project ATHENA Creates Surrogate Human Organ Systems

The development of miniature surrogate human organs, coupled with highly sensitive mass spectrometry technologies, could one day revolutionize the way new drugs and toxic agents are studied. “By developing this ‘homo minutus,’ we are stepping beyond the need for animal or Petri dish testing: There are huge benefits in developing drug and toxicity analysis systems that can mimic the response of actual human organs,” said Rashi Iyer, a senior scientist at Los Alamos National Laboratory. ATHENA, the Advanced Tissue-engineered Human Ectypal Network Analyzer project team, is nearing the full integration of four human organ constructs — liver, heart, lung and kidney — each organ component is about the size of a smartphone screen, and the whole ATHENA “body” of interconnected organs will fit neatly on a desk. A new video available on the Los Alamos National Laboratory YouTube channel updates the ATHENA project as it begins to integrate the various organ systems into a single system. Some 40 percent of pharmaceuticals fail their clinical trials and there are thousands of chemicals whose effects on humans are simply unknown. Providing a realistic, cost-effective and rapid screening system such as ATHENA with high-throughput capabilities could provide major benefits to the medical field, screening more accurately and offering a greater chance of clinical trial success. ATHENA is funded by the Defense Threat Reduction Agency (DTRA) and is a collaboration of Los Alamos National Laboratory, Harvard University, Vanderbilt University, Charité Universitätsmedizin, Berlin, Germany, CFD Research Corporation, and the University of California San Francisco.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Stable Element Doping of Sol-gel Toward Simulating Environmental Matrix in Surrogate Explosive Nuclear Debris

Training nuclear forensic analysis personnel in post-detonation scenarios is of critical importance to nuclear threat response capabilities. Thus, realistic nuclear debris simulants which resemble the size, color, elemental composition, and radionuclide content of actual nuclear fallout from a recent detonation would be valuable for training nuclear first responders in realistic scenarios. As nuclear fallout types vary significantly in each of these parameters based on detonation environment (rural, urban, maritime etc.) and collection location, the ability to tailor each of these parameters accurately in simulated debris would be of immense benefit to the post-detonation analysis community for training both in-field collections and triage and the validation of laboratory level nuclear forensic techniques. Sol-gel synthesis techniques can provide the tunability of size, shape and composition required for producing surrogate nuclear debris of a wide variety. The sol-gel process consists of forming a metal oxide material, often silica, through polymerization of a metal-alkoxy precursor. In this work, we characterize the ability to load the sol-gel particles with secondary elemental components such as iron, aluminum, and calcium toward approximating the elemental composition of debris from various detonation environments and demonstrate the ability to produce particles with controllable size, shape, and color. We also demonstrate quantitative radionuclide encapsulation toward reproducing the radionuclide content of actual fallout from a recent detonation. Finally, we then employ these techniques in producing simulated aerodynamic debris samples with realistic elemental matrix composition and radionuclide content simulating a recent uranium-fueled detonation taking place in a rural desert environment and compare it to historic fallout from the Nevada Nuclear Security Site.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Surrogate Model Based Optimization for Finding Robust Deep Learning Model Architectures

Deep Learning (DL) models are increasingly used throughout the sciences. However, their performance and usefulness depend greatly on their architecture which is defined by hyperparameters such as the number of nodes, layers, the learning rate, etc. Tuning these hyperparameters is time-consuming because evaluating their performance requires a lengthy training step. Stochastic optimizers used in training lead to performance variability and potentially prediction reliability issues. In this talk, we will describe an automated optimization method based on surrogate models and active learning strategies for tuning DL model architectures. We take into account the prediction variability with the goal to identify architectures that make reliable and robust predictions. We demonstrate our developments on an application arising in particle physics.

deep learning↗
DOE OSTI2022

Stable Element Doping of Sol-gel Toward Simulating Environmental Matrix in Surrogate Explosive Nuclear Debris

Training nuclear forensic analysis personnel in post-detonation scenarios is of critical importance to nuclear threat response capabilities. Thus, realistic nuclear debris simulants which resemble the size, color, elemental composition, and radionuclide content of actual nuclear fallout from a recent detonation would be valuable for training nuclear first responders in realistic scenarios. As nuclear fallout types vary significantly in each of these parameters based on detonation environment (rural, urban, maritime etc.) and collection location, the ability to tailor each of these parameters accurately in simulated debris would be of immense benefit to the post-detonation analysis community for training both in-field collections and triage and the validation of laboratory level nuclear forensic techniques. Sol-gel synthesis techniques can provide the tunability of size, shape and composition required for producing surrogate nuclear debris of a wide variety. The sol-gel process consists of forming a metal oxide material, often silica, through polymerization of a metal-alkoxy precursor. In this work, we characterize the ability to load the sol-gel particles with secondary elemental components such as iron, aluminum, and calcium toward approximating the elemental composition of debris from various detonation environments and demonstrate the ability to produce particles with controllable size, shape, and color. We also demonstrate quantitative radionuclide encapsulation toward reproducing the radionuclide content of actual fallout from a recent detonation. Finally, we then employ these techniques in producing simulated aerodynamic debris samples with realistic elemental matrix composition and radionuclide content simulating a recent uranium-fueled detonation taking place in a rural desert environment and compare it to historic fallout from the Nevada Nuclear Security Site.

36 MATERIALS SCIENCE↗